Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Anti-Compensatory Saccades Changes After Visuo-Vestibular Physical Therapy in People With Acute Unilateral Vestibulopathy: A Prospective Observational Study.

Physiotherapy research international : the journal for researchers and clinicians in physical therapy·2026
Same author

Steps against the burden of Parkinson's disease (StepuP): Protocol of a randomized controlled trial elucidating the biomechanical and neurophysiological mechanisms of a speed dependent treadmill training intervention.

PloS one·2026
Same author

ActiTect: a generalizable machine learning pipeline for REM sleep behavior disorder screening through standardized actigraphy.

NPJ digital medicine·2026
Same author

Whole body analysis of functional communities and topological features of gait with different speeds in Parkinson's disease.

Journal of neurology·2026
Same author

Body placement of inertial measurement units differentially affects physical activity assessment accuracy in drug-naïve Parkinson's disease.

Scientific reports·2026
Same author

Deep learning for freezing of gait assessment using inertial measurement units: a multicentre validation study.

NPJ Parkinson's disease·2026

Related Experiment Video

Updated: Aug 2, 2025

Home-Based Monitor for Gait and Activity Analysis
07:24

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

6.8K

A robust walking detection algorithm using a single foot-worn inertial sensor: validation in real-life settings.

Gaëlle Prigent1, Kamiar Aminian2, Andrea Cereatti3,4

  • 1Laboratory of Movement Analysis and Measurement (LMAM), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland. gaelle.prigent@epfl.ch.

Medical & Biological Engineering & Computing
|April 17, 2023
PubMed
Summary
This summary is machine-generated.

A new algorithm accurately detects walking using a single foot-worn inertial measurement unit (IMU) in real-world settings. This validated method enables reliable ambulatory monitoring of gait and mobility outside the lab.

Keywords:
Adaptive thresholdContinuous wavelet transformFoot-worn sensorReal-worldWalking detection

More Related Videos

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
10:52

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

8.9K
Asymmetric Walkway: A Novel Behavioral Assay for Studying Asymmetric Locomotion
08:19

Asymmetric Walkway: A Novel Behavioral Assay for Studying Asymmetric Locomotion

Published on: January 15, 2016

8.9K

Related Experiment Videos

Last Updated: Aug 2, 2025

Home-Based Monitor for Gait and Activity Analysis
07:24

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

6.8K
Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
10:52

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

8.9K
Asymmetric Walkway: A Novel Behavioral Assay for Studying Asymmetric Locomotion
08:19

Asymmetric Walkway: A Novel Behavioral Assay for Studying Asymmetric Locomotion

Published on: January 15, 2016

8.9K

Area of Science:

  • Biomechanics and Movement Science
  • Wearable Technology
  • Digital Health

Background:

  • Gait parameters and walking activity are crucial for mobility assessment.
  • Current gait assessments in lab settings do not fully represent real-world performance.
  • There is a need for validated algorithms for ambulatory monitoring of gait in free-living conditions.

Purpose of the Study:

  • To validate a robust walking detection algorithm using a single foot-worn inertial measurement unit (IMU).
  • To assess the algorithm's performance in real-life settings during free-living activities.

Main Methods:

  • Utilized a challenging dataset from 18 individuals engaged in free-living activities.
  • Employed a multi-sensor wearable system (pressure insoles, IMUs, infrared distance sensors) as the reference standard.
  • Developed and validated a walking detection algorithm based on data from a single foot-worn IMU.

Main Results:

  • Achieved accurate walking detection with high performance metrics.
  • Demonstrated a sensitivity of 98% and a specificity of 91% for walking detection.
  • The algorithm proved robust in distinguishing walking activity during free-living conditions.

Conclusions:

  • A validated walking detection algorithm using a single IMU is feasible for real-world ambulatory monitoring.
  • This algorithm can process raw data into meaningful walking and mobility outcomes.
  • Enables objective assessment of patient performance and gait quality in natural environments.